import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

gtd = pd.read_excel(r'/Users/siddhesh/Library/CloudStorage/OneDrive-UniversityofPittsburgh/Classes/InfoViz/globalterrorismdb_0522dist.xlsx')
gtd.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 209706 entries, 0 to 209705
Columns: 135 entries, eventid to related
dtypes: datetime64[ns](1), float64(54), int64(23), object(57)
memory usage: 216.0+ MB

gtd.head()

	eventid	iyear	imonth	iday	approxdate	extended	resolution	country	country_txt	region	...	addnotes	scite1	scite2	scite3	dbsource	INT_LOG	INT_IDEO	INT_MISC	INT_ANY	related
0	197000000001	1970	7	2	NaN	0	NaT	58	Dominican Republic	2	...	NaN	NaN	NaN	NaN	PGIS	0	0	0	0	NaN
1	197000000002	1970	0	0	NaN	0	NaT	130	Mexico	1	...	NaN	NaN	NaN	NaN	PGIS	0	1	1	1	NaN
2	197001000001	1970	1	0	NaN	0	NaT	160	Philippines	5	...	NaN	NaN	NaN	NaN	PGIS	-9	-9	1	1	NaN
3	197001000002	1970	1	0	NaN	0	NaT	78	Greece	8	...	NaN	NaN	NaN	NaN	PGIS	-9	-9	1	1	NaN
4	197001000003	1970	1	0	NaN	0	NaT	101	Japan	4	...	NaN	NaN	NaN	NaN	PGIS	-9	-9	1	1	NaN

5 rows × 135 columns

gtd_2020 = gtd[gtd['iyear'] == 2020]
gtd_2020.head()

	eventid	iyear	imonth	iday	approxdate	extended	resolution	country	country_txt	region	...	addnotes	scite1	scite2	scite3	dbsource	INT_LOG	INT_IDEO	INT_MISC	INT_ANY	related
201268	202001010001	2020	1	1	NaN	0	NaT	141	Nepal	6	...	NaN	"Unidentified groups torch Ncell towers," EKan...	"Unidentified group sets fire on Ncell tower i...	NaN	START Primary Collection	0	0	0	0	NaN
201269	202001010002	2020	1	1	NaN	0	NaT	141	Nepal	6	...	NaN	"Chand-led party cadres, police exchange fire ...	"Improvised explosive device foundat Ncell tow...	NaN	START Primary Collection	0	0	0	0	NaN
201270	202001010003	2020	1	1	NaN	0	NaT	200	Syria	10	...	NaN	"Shelling hit school killing 6 in rebel-held S...	NaN	NaN	START Primary Collection	-9	-9	0	-9	NaN
201271	202001010005	2020	1	1	NaN	0	NaT	4	Afghanistan	6	...	NaN	"1st LD Writethru- Roadside bomb kills 3 civil...	NaN	NaN	START Primary Collection	0	0	0	0	NaN
201272	202001010006	2020	1	1	NaN	0	NaT	19	Bangladesh	6	...	NaN	"Two crude bombs explode in Ramna," Dhaka Trib...	"Two crude bombs explode near Chhatra Dal’s ev...	NaN	START Primary Collection	-9	-9	0	-9	NaN

5 rows × 135 columns

fig, ax = plt.subplots(figsize=(7,12))

sns.set(style='darkgrid')

graph = sns.countplot(data = gtd_2020, y='region_txt', order = gtd_2020['region_txt'].value_counts().index, 
                      hue = 'attacktype1_txt')

for container in graph.containers:
    graph.bar_label(container, fontsize=7)
#graph.bar_label(graph.containers[0])

plt.title('Terrorist Attacks in 2020', fontsize = 20, fontweight='bold')
plt.xlabel('Count',fontsize = 15, fontweight='bold')
plt.ylabel('Region', fontsize = 15, fontweight='bold')
 
plt.legend(title='Attack Type', loc='lower right') 
plt.show()

gtd_sa = gtd[gtd['region_txt'] == 'South Asia']
gtd_sa

	eventid	iyear	imonth	iday	approxdate	extended	resolution	country	country_txt	region	...	addnotes	scite1	scite2	scite3	dbsource	INT_LOG	INT_IDEO	INT_MISC	INT_ANY	related
585	197011010001	1970	11	1	NaN	0	NaT	153	Pakistan	6	...	NaN	NaN	NaN	NaN	PGIS	-9	-9	1	1	NaN
1186	197202220004	1972	2	22	NaN	0	NaT	92	India	6	...	NaN	NaN	NaN	NaN	Hijacking DB	1	1	1	1	NaN
1862	197305010002	1973	5	1	NaN	0	NaT	4	Afghanistan	6	...	NaN	NaN	NaN	NaN	PGIS	0	1	1	1	NaN
2216	197402020001	1974	2	2	NaN	1	1974-02-03	153	Pakistan	6	...	NaN	NaN	NaN	NaN	PGIS	-9	-9	1	1	NaN
2704	197412090001	1974	12	9	NaN	0	NaT	153	Pakistan	6	...	NaN	NaN	NaN	NaN	PGIS	-9	-9	1	1	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
209691	202012310004	2020	12	31	NaN	0	NaT	4	Afghanistan	6	...	Casualties conflict across sources. Following ...	"Afghan War Casualty Report: December 2020," N...	"Afghan media highlights 31 Dec 20," BBC Monit...	"Terrorism Digest: 30-31 Dec 20," BBC Monitori...	START Primary Collection	-9	-9	0	-9	NaN
209692	202012310005	2020	12	31	NaN	0	NaT	92	India	6	...	NaN	"Police access CCTV footage, get leads in jewe...	"Goldsmith shot dead, CRPF officer injured in ...	"Jeweller shot dead in Srinagar," Daily Excels...	START Primary Collection	0	0	0	0	NaN
209693	202012310006	2020	12	31	NaN	0	NaT	92	India	6	...	The victims included Hardayal Yadav.	"Jeweller shot dead in Srinagar," Daily Excels...	"Goldsmith shot dead by militants in Srinagar,...	"CRPF officer injured in firing by militants i...	START Primary Collection	-9	-9	0	-9	NaN
209700	202012310014	2020	12	31	NaN	0	NaT	141	Nepal	6	...	NaN	"Govt vehicle torched in Jajarkot," Khabar Hub...	NaN	NaN	START Primary Collection	-9	-9	0	-9	NaN
209704	202012310018	2020	12	31	NaN	0	NaT	4	Afghanistan	6	...	NaN	"Civil society activist and tribal elder kille...	"Terrorism Digest: 1-2 Jan 21," BBC Monitoring...	NaN	START Primary Collection	-9	-9	0	-9	NaN

54725 rows × 135 columns

gtd_sa.rename(columns={'iyear':'Year','imonth':'Month','iday':'Day','country_txt':'Country',
                       'region_txt':'World_Region','attacktype1_txt':'Attack_Type','target1':'Target',
                       'nkill':'Killed','nwound':'Wounded','summary':'Summary','gname':'Militant_Group',
                       'targtype1_txt':'Target_Type','weaptype1_txt':'Weapon_Type','motive':'Motive'},inplace=True)

gtd_sa =gtd_sa[['Year','Month','Day','Country','World_Region','city','latitude','longitude','Attack_Type','Killed',
       'Wounded','Target','extended','Militant_Group','Target_Type','Weapon_Type','Motive']]

/var/folders/qr/2xmc9mzj767bg9z6g_q4k9180000gn/T/ipykernel_82517/2436188294.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  gtd_sa.rename(columns={'iyear':'Year','imonth':'Month','iday':'Day','country_txt':'Country',

gtd_sa.head()

	Year	Month	Day	Country	World_Region	city	latitude	longitude	Attack_Type	Killed	Wounded	Target	extended	Militant_Group	Target_Type	Weapon_Type	Motive
585	1970	11	1	Pakistan	South Asia	Karachi	24.891115	67.143311	Assassination	4.0	20.0	Deputy Foreign Minister: Zygfryd Wolniak	0	Unknown	Government (Diplomatic)	Vehicle (not to include vehicle-borne explosiv...	NaN
1186	1972	2	22	India	South Asia	New Delhi	28.585836	77.153336	Hijacking	0.0	0.0	B-747	0	Palestinians	Airports & Aircraft	Explosives	NaN
1862	1973	5	1	Afghanistan	South Asia	Kabul	34.516895	69.147011	Unknown	0.0	1.0	unk, asst. mgr., Indian Airlines, Kabul	0	Black December	Airports & Aircraft	Unknown	NaN
2216	1974	2	2	Pakistan	South Asia	Karachi	24.891115	67.143311	Bombing/Explosion	0.0	0.0	Greek Freighter in Karachi	1	Muslim Guerrillas	Maritime	Firearms	NaN
2704	1974	12	9	Pakistan	South Asia	Peshawar	34.006004	71.537430	Bombing/Explosion	0.0	2.0	USIS Center	0	Unknown	Government (Diplomatic)	Explosives	NaN

gtd_sa_groups=gtd_sa[gtd_sa['Militant_Group'].isin(gtd_sa['Militant_Group'].value_counts()[1:11].index)]
pd.crosstab(gtd_sa_groups.Year, gtd_sa_groups.Militant_Group).plot(color=sns.color_palette('Paired',10))

sns.set(style='darkgrid')
fig=plt.gcf()
fig.set_size_inches(18,6)

plt.title('South Asia - Top Terror Operators Over the Years', fontsize = 15, fontweight='bold')
plt.xlabel('Year',fontsize = 12, fontweight='bold')
plt.ylabel('Count', fontsize = 12, fontweight='bold')

plt.show()

import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
pio.renderers.default = 'vscode+notebook'

gtd_sa_map = gtd_sa['Militant_Group'].value_counts()[1:11].index
filtered_data = gtd_sa[gtd_sa['Militant_Group'].isin(gtd_sa_map)]

fig = px.scatter_mapbox(filtered_data, lat='latitude', lon='longitude',
                        hover_name='Militant_Group', color='Militant_Group',
                        title='Attacks of Militant Groups across South Asia',
                        zoom=1, height=800)

fig.update_layout(mapbox_style="carto-positron")
fig.update_geos(projection_type="orthographic")
fig.update_traces(marker=dict(size=10, opacity=0.5))

fig.show(renderer="notebook")